Reconfigurable hardware implementation of a phase-correlation stereoalgorithm

This paper describes the implementation of a stereo-vision system using Field Programmable Gate Arrays (FPGAs). Reconfigurable hardware, including FPGAs, is an attractive platform for implementing vision algorithms due to its ability to exploit parallelism often found in these algorithms, and due to the speed with which applications can be developed as compared to hardware. The system outputs 8-bit, subpixel disparity estimates for 256× 360 pixel images at 30,fps. A local-weighted phase correlation algorithm for stereo disparity [Fleet, D. J.: {Int. Conf. Syst. Man Cybernetics 1:48–54 (1994)] is implemented. Despite the complexity of performing correlations on multiscale, multiorientation phase data, the system runs as much as 300 times faster in hardware than its software implementation. This paper describes the hardware platform used, the algorithm, and the issues encountered during its hardware implementation. Of particular interest is the implementation of multiscale, steerable filters, which are widely used in computer vision algorithms. Several trade-offs (reducing the number of filter orientations from three to two, using fixed-point computation, changing the location of one localized low-pass filter, and using L1 instead of L2 norms) were required to both fit the design into the available hardware and to achieve video-rate processing. Finally, results from the system are given both for synthetic data sets as well as several standard stereo-pair test images.     

High-k材料MOS器件的PISCES-II模拟

High-k材料是指介电常数k高于SiO2的材料。使用high-k材料做栅绝缘层,是减小MOS器件栅绝缘层直接隧道击穿(DirectTunneling,DT)电流的有效方法。文章在二维器件模拟软件PISCES-II中添加了模拟以high-k材料为栅绝缘层的MOS器件模型,并对SiO2和high-k材料的MOS晶体管器件特性进行了模拟比较,成功地验证了所加high-k材料MOS器件模型的正确性。改进后的PISCES-II程序,可以方便地对以各种high-k材料为栅绝缘层的器件性能进行模拟。     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.     

反应注射充模特性及模具浇口结构设计的研究

本文对反应注射充模过程中的铺展流、射流、绕流和气泡的特性进行了分析研究，并讨论了模具浇口结构及位置对充模的影响，给出了制品的设计方法。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

Gate location design in injection molding of an automobile junction box with integral hinges

Polymers such as polypropylene or polyethylene offer a unique feature of producing an integral or living hinge for automobiles, which can flex over a million times without causing a failure. However, due to increased fluidity resistance at hinges during molding, several defects such as short shot or premature failure can occur with the improper selection of gate locations. In this paper, a design guideline was induced by investigating resin flow patterns depending on several gate positions obtained by numerical analyses of a simple strip with a hinge. The analyses of the simple strip part showed that the resin at the hinge did not flow until the other side of the strip was filled. Once the resin at the hinge did not flow for a long time enough to be solidified, defects such as short shots or hesitation marks formed. For a practical application of the design guideline determined, four gate systems for an automobile junction box were designed. It was found that the properly determined gate location leads to better resin flow and shorter hesitation time. Finally, injection molding tryouts using a mold that was designed by one of the proposed gate systems were conducted. The experiments showed that hinges without defects could be produced by using the designed gate location to assure the induced design guideline to be reasonable.     

自动化远程监控系统在金鸡拦河闸的应用

金鸡拦河闸远程监控系统利用通信技术、图像处理技术、分布式数据库技术、网页多媒体制作技术、计算机实时监控技术等,实现对水闸实行水文数据实时采集、分析处理和闸门自动启闭控制以及实时图像监控,提高了金鸡拦河闸工程水资源的优化调配水平以及防洪抗灾能力.     

市民广场景观湖泄水闸工程闸门和启闭机的布置与设计

简述江西省宜春市市民广场景观湖泄水闸工程设计.在设计过程中,着重考虑了闸门和启闭机选型是否合理,以满足工程的安全性和美观性.     

中职学校计算机教学中值得重视的四个问题

随着当今社会的飞速发展,计算机的发展愈来愈迅速,应用也愈来愈普及,中职学校的计算机专业的很多课程已渗透到其他专业,计算机教学已越来越普及、重要。本文根据作者多年计算机教学的体会,简单总结和分析了在中职学校计算机教学中值得重视的强化学生学习计算机知识的意识、合理安排计算机课程比例、培养学生对计算机知识的自学能力、考核计算机教学中的实践能力等四个问题。